Only a few works exploit edge features in graph neural networks, and all of them have obvious limitations. Schlichtkrull et al.[1]} proposed R-GCNs to process modeling relational data. However, the models can accept graphs only when their edges are labeled, which indicates that the edges cannot include continuous attributes. Gong et al.[2]} presented a framework enhances GCNs[3]} and GATs[4]}. This framework can accept continuous attributes of edges, whereas it merely regards them as weights between different node pairs. In most cases, it is somewhat unreasonable. For example, a special graph can be constructed, with the same features for nodes and different features for edges. If we consider edge features as weights and all weights of each node sum as one, it is interesting to find that no matter how the node features update, it will remain unchanged during the learning process.
